Construction of a Nomogram Prediction Model for Failure of Extubation in Severe Pneumonia Based on Diaphragmatic Ultrasound Cardiac Parameters and Blood Gas Indices
WEI Weiqin, HU Xiaochun, FANG Donghai, et al
The Affiliated Hospital of Guizhou Medical University, Guizhou Guiyang 550004, China
Abstract：Objective: To explore the value of combined phrenic ultrasonography, cardiac parameters, and blood gas indexes in predicting the failure of offline extubation of severe pneumonia, and to construct a nomogram prediction model for early clinical intervention.Methods: A total of 210 patients with severe pneumonia in the Affiliated Hospital of Guizhou Medical University from March 2021 to December 2023 were selected as the study subjects, and the study subjects were randomly divided into a training set (70%, 147 cases) and a verification set (30%, 63 cases). The general information of the patients was analyzed. The lasso-Logistic regression equation was used to screen the predictors of offline extubation failure for severe pneumonia, and a nomogram prediction model was constructed. The receiver operating characteristic curve (ROC), decision curve (DCA), and calibration curve were used to analyze the efficacy of the model.Results: In the training set and the validation set, the differences in age, mechanical ventilation time, APACHE Ⅱ score, ICU stay, CRP/ALB, cardiac parameters, diaphragm ultrasound parameters, PaO2, PaCO2, P/F, PA-aO-2, and history of underlying cardiopulmonary disease were statistically significant when comparing the case group (failed extubation off the machine) with the control group (successful extubation off the machine) (P<0.05); Logistic regression equation showed that age, DTF, DE, E/A, PaO2, PaCO2, CRP/ALB, and basic history of cardiopulmonary disease were all influencing factors for the failure of offline extubation of severe pneumonia (P<0.05). The nomogram prediction model for offline extubation failure for severe pneumonia was obtained by visualization using R language software. The AUC of the nomogram prediction model was 0.866 (95%CI:0.801-0.930) in the training set and 0.917 (95%CI:0.853-0.982) in the verification set, respectively. The calibration curve was close to the 48° reference line, the prediction points were evenly distributed, and the DCA curve was within the range of 0.35 ~ 0.8. The nomogram prediction model could obtain the greatest benefits in both the training set and the verification set.Conclusion: This nomogram prediction model based on diaphragm ultrasound, heart parameters, and blood gas indexes can be used to predict the risk of offline extubation of severe pneumonia at an early stage. Accordingly, appropriate intervention plans can be made and the prognosis is improved.
韦卫琴, 胡晓纯, 房东海, 张运铎, 代传扬, 张燕, 周永芳. 基于膈肌超声心脏参数血气指标构建重症肺炎脱机拔管失败的nomogram预测模型[J]. 河北医学, 2024, 30(9): 1519-1524.
WEI Weiqin, HU Xiaochun, FANG Donghai, et al. Construction of a Nomogram Prediction Model for Failure of Extubation in Severe Pneumonia Based on Diaphragmatic Ultrasound Cardiac Parameters and Blood Gas Indices. HeBei Med, 2024, 30(9): 1519-1524.
2024, Vol. 30 
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